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Surveys were conducted in the five southern rice-producing states of Arkansas, Louisiana, Mississippi, Missouri and Texas in the United States during the 2018 and 2019 cropping seasons to determine the distribution and pathogenicity of fungal pathogens associated with seedling blight in rice. A total of 349 pathogenic fungal isolates were collected and identified as belonging to four genera: Rhizoctonia solani, Fusarium spp., Sclerotium rolfsii and Marasmius graminum based on morphological characteristics, molecular analysis and Koch's postulates. R. solani (252 out of 349 pathogenic isolates) was the most prevalent fungus isolated from diseased samples. Of the 252 pathogenic R. solani isolates, 245 were further classified as anastomosis group 11 (AG-11) and 7 as AG-4. Isolates of R. solani AG-4 and M. graminum were the most aggressive, with the highest stand loss (63% to 100%) and median disease rating (DR; 5.0), followed by isolates of R. solani AG-11 (stand loss = 4% to 100% and DR = 0.6 to 5.0), Fusarium spp. (stand loss = 26% to 48% and DR = 2.0 to 5.0) and S. rolfsii (stand loss = 33% to 48% and DR = 2.0 to 3.0) in causing seedling blight in rice. R. solani (62% to 83% of total pathogenic isolates) and Fusarium spp. (10% to 24% of total pathogenic isolates) were predominant in all the five states surveyed. S. rolfsii and M. graminum were present only in Louisiana and Texas. The results of this first systematic survey of rice seedling diseases in the southern United States will help develop effective fungicide seed treatment strategies for control of stand loss caused by seedling blight, one of the major factors limiting rice production.  相似文献   
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Journal of Pest Science - Queensland fruit fly (Q-fly), Bactrocera tryoni (Froggatt), is a significant pest of horticultural crops in Australia. The sterile insect technique (SIT) is currently...  相似文献   
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Background

The proportion of granule types in wheat starch is an important characteristic that can affect its functionality. It is widely accepted that granule types are either large, disc-shaped A-type granules or small, spherical B-type granules. Additionally, there are some reports of the tiny C-type granules. The differences between these granule types are due to its carbohydrate composition and crystallinity which is highly, but not perfectly, correlated with the granule size. A majority of the studies that have considered granule types analyse them based on a size threshold rather than chemical composition. This is understandable due to the expense of separating starch into different types. While the use of a size threshold to classify granule type is a low-cost measure, this results in misclassification. We present an alternative, statistical method to quantify the proportion of granule types by a fit of the mixture distribution, along with an R package, a web based app and a video tutorial for how to use the web app to enable its straightforward application.

Results

Our results show that the reliability of the genotypic effects increase approximately 60% using the proportions of the A-type and B-type granule estimated by the mixture distribution over the standard size-threshold measure. Although there was a marginal drop in reliability for C-type granules. The latter is likely due to the low observed genetic variance for C-type granules.

Conclusions

The determination of the proportion of granule types from size-distribution is better achieved by using the mixing probabilities from the fit of the mixture distribution rather than using a size-threshold.
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